Depth-Based Methods.
Depth-based methods perform the 3D detection task using pixel-wise depth maps as an additional input, where the depth maps are precomputed using monocular depth estimation architectures [1]}. Estimated depth maps can be used in combination with images to perform the 3D detection task [2]}, [3]}, [4]}, [5]}. Alternatively, depth maps can be converted to 3D point clouds, commonly known as Pseudo-LiDAR [6]}, which are either used directly  [7]}, [8]} or combined with image information [9]}, [10]} to generate 3D object detection results. Depth-based methods separate depth estimation from 3D object detection during the training stage, leading to the learning of sub-optimal depth maps used for the 3D detection task. Accurate depth should be prioritized for pixels belonging to objects of interest, and is less important for background pixels, a property that is not captured if depth estimation and object detection are trained independently.
